Big Data Programming (SE421) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Big Data Programming SE421 2 2 0 3 5
Pre-requisite Course(s)
CMPE438
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Drill and Practice.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives Upon completing this course, the student will be able to design and implement map-reduce programs for various large data set processing tasks, and will be able to design and implement programs using Apache Spark.
Course Learning Outcomes The students who succeeded in this course;
  • Describe the architecture of Hadoop.
  • Explain the basic operation of HDFS
  • Develop MapReduce applications
  • View HDFS data from a relational perspective using Pig and Hive
  • Describe what Spark is all about know why you would want to use Spark
  • Use Resilient Distributed Datasets (RDD) operations
  • Use Resilient Distributed Datasets (RDD) operations
  • Implement and execute Apache Spark applications.
Course Content What is "Big Data"; the dimensions of Big Data; scaling problems; HDFS and the Hadoop ecosystem; the basics of HDFS, MapReduce and Hadoop cluster; writing MapReduce programs to answer questions about data; MapReduce design patterns; basic Spark architecture; common operations; Use Resilient Distributed Datasets (RDD) operations.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to Big Data and Hadoop Chapter 1
2 Setting Up a Hadoop Cluster Chapter 9
3 Hadoop Distributed Filesystem (HDFS) Chapter 3
4 Hadoop Distributed Filesystem (HDFS) Chapter 4
5 MapReduce Chapter 2
6 MapReduce Chapter 5
7 MapReduce Chapter 6
8 MapReduce Chapter 7-8
9 Administering Hadoop Chapter 10
10 Pig Chapter 11
11 Hive Chapter 12
12 HBase Chapter 13
13 Spark Programming Other resources 2
14 Spark Programming Other resources 2
15 Final Exam
16 Final Exam

Sources

Course Book 1. Hadoop: The Definitive Guide, Tom White, 3rd. Ed., O'Reilly Media, 2012
Other Sources 2. MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, Donald Miner, Adam Shook, O'Reilly Media, November 2012
3. Learning Spark: Lightning-Fast Big Data Analysis, Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, O'Reilly Media, January 2015

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory 5 30
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 7 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses
Major Area Courses X
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Adequate knowledge in mathematics, science and subjects specific to the software engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. X
4 The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; the ability to utilize information technologies effectively. X
5 The ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline. X
6 The ability to work effectively in inter/inner disciplinary teams; ability to work individually. X
7 Effective oral and written communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8 The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
9 Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development X
10 The ability to behave according to ethical principles, awareness of professional and ethical responsibility. X
11 Knowledge of the standards utilized in software engineering applications.
12 Knowledge on business practices such as project management, risk management and change management. X
13 Awareness about entrepreneurship, and innovation.
14 Knowledge on sustainable development.
15 Knowledge of the effects of software engineering applications on the universal and social dimensions of health, environment, and safety. X
16 Awareness of the legal consequences of engineering solutions.
17 An ability to apply algorithmic principles, mathematical foundations, and computer science theory in the modeling and design of computer-based systems with the trade-offs involved in design choices. X
18 The ability to apply engineering approach to the development of software systems by analyzing, designing, implementing, verifying, validating and maintaining software systems. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory 14 2 28
Application
Special Course Internship
Field Work
Study Hours Out of Class
Presentation/Seminar Prepration
Project
Report
Homework Assignments 5 6 30
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 15 15
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 93